
"""simple docstring"""


import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
						from PIL import Image
else:




						class  __lowerCamelCase  :




										@staticmethod
										def        UpperCAmelCase__						(				*UpperCAmelCase							,						**UpperCAmelCase   ):
													pass






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  hashlib.mda(image.tobytes()						)
			return m.hexdigest()[:10]






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  np.array(A_						)
			lowerCamelCase_					=  npimg.shape
			return {"hash": hashimage(A_						), "shape": shape}




@is_pipeline_test
@require_vision
@require_torch
class  __lowerCamelCase  ( unittest.TestCase			):
				a__:      Optional[Any]          =    dict(
				    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
				a__:      Union[str, Any]          =    dict(
				    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  MaskGenerationPipeline(model=UpperCamelCase__							,						image_processor=UpperCamelCase__   )
							return image_segmenter, [
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							]




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							pass




				@require_tf
				@unittest.skip('''Image segmentation not implemented in TF'''   )
				def        UpperCAmelCase__						(				self   ):
							pass




				@slow
				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  pipeline('''mask-generation'''							,						model='''facebook/sam-vit-huge'''   )

							lowerCamelCase_					=  image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''							,						points_per_batch=256   )

							# Shortening by hashing
							lowerCamelCase_					=  []
							for i, o in enumerate(outputs['''masks''']   ):
										new_outupt += [{"mask": mask_to_test_readable(UpperCamelCase__   ), "scores": outputs["scores"][i]}]

							# fmt: off
							self.assertEqual(
							    nested_simplify(UpperCamelCase__							,						decimals=4   )							,						[
							        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_4_4_4},
							        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_2_1},
							        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_1_6_7},
							        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_1_3_2},
							        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_0_5_3},
							        {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.9_9_6_7},
							        {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.9_9_3},
							        {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.9_9_0_9},
							        {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.9_8_7_9},
							        {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.9_8_3_4},
							        {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.9_7_1_6},
							        {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.9_6_1_2},
							        {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.9_5_9_9},
							        {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.9_5_5_2},
							        {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.9_5_3_2},
							        {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.9_5_1_6},
							        {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.9_4_9_9},
							        {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.9_4_8_3},
							        {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.9_4_6_4},
							        {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (480, 640)}, '''scores''': 0.9_4_3},
							        {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (480, 640)}, '''scores''': 0.9_4_3},
							        {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (480, 640)}, '''scores''': 0.9_4_0_8},
							        {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.9_3_3_5},
							        {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.9_3_2_6},
							        {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.9_2_6_2},
							        {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.8_9_9_9},
							        {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.8_9_8_6},
							        {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.8_9_8_4},
							        {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.8_8_7_3},
							        {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.8_8_7_1}
							    ]							,						)
							# fmt: on





				@require_torch
				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  '''facebook/sam-vit-huge'''
							lowerCamelCase_					=  pipeline('''mask-generation'''							,						model=UpperCamelCase__   )

							lowerCamelCase_					=  image_segmenter(
							    '''http://images.cocodataset.org/val2017/000000039769.jpg'''							,						pred_iou_thresh=1							,						points_per_batch=256   )

							# Shortening by hashing
							lowerCamelCase_					=  []
							for i, o in enumerate(outputs['''masks''']   ):
										new_outupt += [{"mask": mask_to_test_readable(UpperCamelCase__   ), "scores": outputs["scores"][i]}]

							self.assertEqual(
							    nested_simplify(UpperCamelCase__							,						decimals=4   )							,						[
							        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_4_4_4},
							        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_2_1_0},
							        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_1_6_7},
							        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_1_3_2},
							        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_0_5_3},
							    ]							,						)





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			prime_factors(A_   )
  if is_square_free(A_   ):
    return -1 if len(A_   ) % 2 else 1
  return 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()

